Is there a correlation between cloud phase
and surface snowfall rate in GCMs?
Franziska Hellmuth1, Anne Fouilloux1, Anne Sophie Daloz2, Trude Storelvmo1
1 Department of Geosciences, University of Oslo, Norway
2 Center for International Climate Research (CICERO), Norway franziska.hellmuth@geo.uio.no
Why is it important to present cloud phase and
snowfall accurately? Is snowfall bias in GCMs
relatable to cloud phase bias?
How can we use GCM data using Pangeo?
What was not provided by Pangeo?
Mixed phase clouds are
not well represented in
GCMs.
Ice formation influences
radiative effect,
precipitation formation,
and cloud lifetime.
Objective
Ice water path/Liquid water path (ERA5 - CMIP6)
(g m-2)
Ice water path overestimate Liquid water path underestimate
Ice Liquid
30 year, season: DJF
Why is it important to present cloud phase and snowfall
accurately? Is snowfall bias in GCMs relatable to cloud phase
bias?
How can we use GCM data using Pangeo?
What was not provided by Pangeo?
CMIP6 models and variables (pangeo.io)
17 CMIP6 models
Horizontal resolution 100 km
Why is it important to present cloud phase and snowfall
accurately? Is snowfall bias in GCMs relatable to cloud phase
bias?
How can we use GCM data using Pangeo?
What was not provided by Pangeo?
Horizontal grid (CMIP6 to NorESM2-MM)
https://tinyurl.com/regridder
Vertical grid (hybrid-σ-pressure to isobaric-pressure)
https://tinyurl.com/hybridtopressure
Pangeo provides good amount of functions
Vertical interpolation:
geocat.comp.interp_hybrid_to_pressure
Horizontal interpolation: xesmf.Regridder
franziska.hellmuth@geo.uio.no, EGU22-8037
Key points
Next steps
Find mixed-phase clouds in CMIP6
Relate mixed-phase clouds to surface snowfall
Include satellite data (CloudSat)
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